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Fix tensor topk param #9703

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merged 4 commits into from
Jan 6, 2023
Merged

Fix tensor topk param #9703

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0698776
set default dim param as None instead of -1, move tensor.topk to CPython
marigoold Jan 5, 2023
01c8f65
set default dim param as None instead of -1, move tensor.topk to CPython
marigoold Jan 5, 2023
bfdd03a
auto format by CI
oneflow-ci-bot Jan 5, 2023
8aada97
Merge branch 'master' into fix_tensor_topk_param
mergify[bot] Jan 5, 2023
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2 changes: 2 additions & 0 deletions oneflow/api/python/framework/tensor_functions.cpp
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Expand Up @@ -332,6 +332,7 @@ DIRECT_PASS_FUNC(PyTensorObject_bincount, functional::bincount)
DIRECT_PASS_FUNC(PyTensorObject_isclose, functional::isclose)
DIRECT_PASS_FUNC(PyTensorObject_broadcast_to, functional::broadcast_to)
DIRECT_PASS_FUNC(PyTensorObject_unique, functional::unique)
DIRECT_PASS_FUNC(PyTensorObject_topk, functional::topk)

// functions that parsing at Python C api layer
static PyObject* PyTensorObject_byte(PyObject* self, PyObject* unused) {
Expand Down Expand Up @@ -1018,6 +1019,7 @@ PyMethodDef PyTensorObject_extra_methods[] = {
{"isclose", (PyCFunction)PyTensorObject_isclose, METH_VARARGS | METH_KEYWORDS, NULL},
{"broadcast_to", (PyCFunction)PyTensorObject_broadcast_to, METH_VARARGS | METH_KEYWORDS, NULL},
{"unique", (PyCFunction)PyTensorObject_unique, METH_VARARGS | METH_KEYWORDS, NULL},
{"topk", (PyCFunction)PyTensorObject_topk, METH_VARARGS | METH_KEYWORDS, NULL},

// macro UNARY_METHOD
{"abs", PyTensorObject_abs, METH_NOARGS, NULL},
Expand Down
2 changes: 1 addition & 1 deletion oneflow/core/functional/functional_api.yaml
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Expand Up @@ -2647,7 +2647,7 @@
bind_python: True

- name: "topk"
signature: "TensorTuple[values, indices] (Tensor input, Int32 k, Int32 dim=-1, Bool largest=True, Bool sorted=True) => TopK"
signature: "TensorTuple[values, indices] (Tensor input, Int32 k, Int32 dim=None, Bool largest=True, Bool sorted=True) => TopK"
bind_python: True

- name: "in_top_k"
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10 changes: 6 additions & 4 deletions oneflow/core/functional/impl/array_functor.cpp
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Expand Up @@ -3118,16 +3118,18 @@ class ToDeviceFunctor {
class TopKFunctor {
public:
TopKFunctor() { op_ = CHECK_JUST(one::OpBuilder("top_k").Input("in").Output("out").Build()); }
Maybe<TensorTuple> operator()(const std::shared_ptr<Tensor>& input, const int32_t& k,
const int32_t& dim, const bool largest, const bool sorted) const {
Maybe<TensorTuple> operator()(const std::shared_ptr<Tensor>& input, const int32_t k,
const Optional<int32_t>& dim, const bool largest,
const bool sorted) const {
auto outputs = std::make_shared<TensorTuple>(2);
std::shared_ptr<Tensor> values;
std::shared_ptr<Tensor> indices;

auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("k", "sorted");
attrs.SetAllAttrs(k, sorted);

int32_t axis = dim;
int32_t dim_value = dim.value_or(-1);
int32_t axis = dim_value;
axis = JUST(maybe_wrap_dim(axis, input->ndim()));
if (axis == input->ndim() - 1) {
if (largest) {
Expand All @@ -3139,7 +3141,7 @@ class TopKFunctor {
values = JUST(DimGather(input, axis, indices, false));

} else {
auto perm = JUST(GetPermWhenTransposeAxisToLastDim(input->ndim(), dim));
auto perm = JUST(GetPermWhenTransposeAxisToLastDim(input->ndim(), dim_value));
auto x = JUST(Transpose(input, *perm));
if (largest) {
indices = JUST(OpInterpUtil::Dispatch<Tensor>(*op_, {x}, attrs));
Expand Down
5 changes: 0 additions & 5 deletions python/oneflow/framework/tensor.py
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Expand Up @@ -402,10 +402,6 @@ def _T(self):
return flow._C.T(self)


def _topk(self, k, dim: int = None, largest: bool = True, sorted: bool = True):
return flow.topk(self, k, dim, largest, sorted)


def _nms(boxes, scores, iou_threshold: float):
return flow.nms(boxes, scores, iou_threshold)

Expand Down Expand Up @@ -627,7 +623,6 @@ def RegisterMethods():
Tensor.eq = _eq
Tensor.sort = _sort
Tensor.tolist = _tolist
Tensor.topk = _topk
Tensor.nms = _nms
Tensor.nonzero = _nonzero
Tensor.prod = _prod
Expand Down
6 changes: 3 additions & 3 deletions python/oneflow/test/tensor/test_tensor_part_1.py
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Expand Up @@ -1035,9 +1035,9 @@ def test_tensor_topk_with_random_data(test_case):
x = random_tensor(ndim=4, dim1=8, dim2=9, dim3=10).to(device)
y = x.topk(
random(low=1, high=8).to(int),
dim=random(low=1, high=4).to(int),
largest=random_bool(),
sorted=constant(True),
dim=random(low=1, high=4).to(int) | nothing(),
largest=random_bool() | nothing(),
sorted=constant(True) | nothing(),
)
return y[0], y[1]

Expand Down